Improving the quantification of sediment source contributions using different mathematical models and spectral preprocessing techniques for individual or combined spectra of ultraviolet-visible, near-and middle-infrared spectroscopy

Autor: Cláudia Alessandra Peixoto de Barros, Fábio Farias Amorim, Jean Michel Moura-Bueno, Tales Tiecher, Laurent Caner, Olivier Evrard, Gabriela Naibo, Yuri Jacques Agra Bezerra da Silva, Jean Paolo Gomes Minella, Danilo dos Santos Rheinheimer, Rafael Ramon
Přispěvatelé: Universidade Federal do Rio Grande do Sul [Porto Alegre] (UFRGS), Universidade Federal de Santa Maria (UFSM), Institut de Chimie des Milieux et Matériaux de Poitiers (IC2MP), Université de Poitiers-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Hydrogéologie, Argiles, Sols, Altérations (E2) (HydrASA), Université de Poitiers-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université de Poitiers-Institut national des sciences de l'Univers (INSU - CNRS)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Géochimie Des Impacts (GEDI), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Universidade Federal do Piauí (UFPI), Universidade Federal Rural de Pernambuco (UFRPE), CNPq, Universidade Federal de Santa Maria = Federal University of Santa Maria [Santa Maria, RS, Brazil] (UFSM), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Poitiers-Institut de Chimie du CNRS (INC), Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Poitiers-Institut de Chimie du CNRS (INC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Poitiers-Institut de Chimie du CNRS (INC), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Normalization (statistics)
Multivariate statistics
Soil test
Soil Science
Vis-NIR-MIR database
010501 environmental sciences
[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study
01 natural sciences
Alternative fingerprints
TRACER
Machine learning
Spectral preprocessing
[SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology
[SDU.STU.HY]Sciences of the Universe [physics]/Earth Sciences/Hydrology
Spectroscopy
[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces
environment
0105 earth and related environmental sciences
Remote sensing
Mathematical model
Sediment
04 agricultural and veterinary sciences
Parametric model
[SDE]Environmental Sciences
040103 agronomy & agriculture
Soil erosion
0401 agriculture
forestry
and fisheries
Environmental science
Zdroj: Geoderma
Geoderma, Elsevier, 2021, 384, pp.114815. ⟨10.1016/j.geoderma.2020.114815⟩
Geoderma, 2021, 384, pp.114815. ⟨10.1016/j.geoderma.2020.114815⟩
ISSN: 0016-7061
1872-6259
DOI: 10.1016/j.geoderma.2020.114815⟩
Popis: International audience; In recent years, several sediment fingerprinting studies have used ultraviolet–visible (UV–Vis), near-infrared (NIR) and middle-infrared (MIR) spectroscopy as a low cost, non-destructive and fast alternative to obtain tracer properties to estimate sediment source contributions. For this purpose, partial last square regression (PLSR) has often been used to build predictive parametric models. However, spectra preprocessing and more robust and non-parametric models such as support vector machines (SVM) has gained little attention in these studies. Accordingly, the objectives of the current research were to evaluate (i) the accuracy of two multivariate methods (PLSR and SVM), (ii) the effect of eight spectra preprocessing techniques, and (iii) the effect of using the information contained in the UV–Vis, NIR and MIR regions considered either separately or in combination on sediment source apportionment. The estimated source contribution was then compared with contributions obtained by the conventional fingerprinting approach based on geochemical tracers. This study was carried out in the Arvorezinha catchment (1.23 km2) located in southern Brazil. Forty soil samples were collected in three main potential source (cropland surface, unpaved roads and stream channels) and twenty-nine suspended sediment samples collected at the catchment outlet during nine rainfall-runoff events were used in this study. Both PLSR and SVM models showed a higher accuracy when calibrated and validated with the spectra submitted to spectral processing when compared to the direct use of the raw spectra. The best model results were obtained with PLSR and SVM mathematical models associated with the spectral preprocessing techniques 1st derivative Savitzky-Golay (SGD1), normalization (NOR) and combining NOR + SGD1 in the UV–Vis + NIR + MIR. The lowest errors were observed when the UV–Vis + NIR + MIR bands were combined due to the gain in information and, consequently, the increase in discriminant power achieved by the models. Despite the good accuracy of the models calibrated and validated with the artificial mixtures, significant errors remain when results of source contributions are compared to those obtained with the conventional sediment fingerprinting technique based on geochemical tracers. Nevertheless, the magnitude of the contributions calculated by the spectroscopy and geochemical approaches remains very similar for all sources, especially when using the SVM-UV–Vis + NIR + MIR model. Therefore, spectroscopy proved to be a fast, cheap and accurate technique, offering an alternative to the conventional geochemical approach for discriminating sediment source contributions in agricultural catchments located in subtropical regions.
Databáze: OpenAIRE
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